Welcome back to my video series on machine learning in Python with scikit-learn. In the previous video, we learned how to search for the optimal tuning parameters for a model using both GridSearchCV and RandomizedSearchCV. In this video, you'll learn how to properly evaluate a classification model using a variety of common tools and metrics, as well as how to adjust the performance of a classifier to best match your business objectives. Here's the agenda: Video #9: How to evaluate ...

Welcome back to my video series on machine learning in Python with scikit-learn. In the previous video, we learned about K-fold cross-validation, a very popular technique for model evaluation, and then applied it to three different types of problems. In this video, you'll learn how to efficiently search for the optimal tuning parameters (or "hyperparameters") for your machine learning model in order to maximize its performance. I'll start by demonstrating an exhaustive "grid search" process using scikit-learn's GridSearchCV class, and ...

Welcome back to my video series on machine learning in Python with scikit-learn. In the previous video, we worked through the entire data science pipeline, including reading data using pandas, visualization using seaborn, and training and interpreting a linear regression model using scikit-learn. We also covered evaluation metrics for regression, and feature selection using the train/test split procedure. In this video, we'll focus on K-fold cross-validation, an incredibly popular (and powerful) machine learning technique for model evaluation. If you've spent ...

Welcome back to my video series on machine learning in Python with scikit-learn. In the previous video, we learned how to choose between classification models (and avoid overfitting) by using the train/test split procedure. In this video, we're going to learn about our first regression model, in which the goal is to predict a continuous response. As well, we'll cover a larger part of the data science pipeline by learning how to ingest data using the pandas library and visualize ...

Welcome back to my video series on machine learning in Python with scikit-learn. In the previous video, we learned how to train three different models and make predictions using those models. However, we still need a way to choose the "best" model, meaning the one that is most likely to make correct predictions when faced with new data. That's the focus of this week's video. Video #5: Comparing machine learning models How do I choose which model to use for ...

Welcome back to my series of video tutorials on effective machine learning with Python's scikit-learn library. In the first three videos, we discussed what machine learning is and how it works, we set up Python for machine learning, and we explored the famous iris dataset. This week, we're going to learn about our first machine learning model and use it to make predictions on the iris dataset! Video #4: Model training and prediction What is the K-nearest neighbors classification model? ...

Welcome back to my new video series on machine learning with scikit-learn. Last week, we discussed the pros and cons of scikit-learn, showed how to install scikit-learn independently or as part of the Anaconda distribution of Python, walked through the IPython Notebook interface, and covered a few resources for learning Python if you don't already know the language. This week, we're going to take our first steps in scikit-learn by loading and exploring the famous Iris dataset! Video #3: Exploring the ...

Last Wednesday, I introduced my new weekly video series, "Introduction to machine learning with scikit-learn". Over the next few months, you'll learn how to perform effective machine learning using Python's scikit-learn library in order to advance your data science skills. I'll be covering machine learning fundamentals and best practices, as well as how to implement those practices using scikit-learn. Last week's video laid the groundwork for the entire series by defining machine learning and explaining how it works. Video #2: ...

Have you tried out a few Kaggle competitions, but you aren't quite sure what you're supposed to be doing? Or perhaps you've heard all the talk in the Kaggle forums about Python's scikit-learn library, but you haven't figured out how to take advantage of this powerful tool for machine learning? If so, this post is for you! As a data science instructor and the founder of Data School, I spend a lot of my time figuring out how to distill ...